The dbpedia Extraction Framework, the generation framework behind one of the Linked Open Data cloud’s central hubs, has limitations which lead to quality issues with the dbpedia dataset. Therefore, we provide a new take on its Extraction Framework that allows for a sustainable and general-purpose Linked Data generation framework by adapting a semantic-driven approach. The proposed approach decouples, in a declarative manner, the extraction, transformation, and mapping rules execution. This way, among others, interchanging different schema annotations is supported, instead of being coupled to a certain ontology as it is now, because the dbpedia Extraction Framework allows only generating a certain dataset with a single semantic representation. In this paper, we shed more light to the added value that this aspect brings. We provide an extracted dbpedia dataset using a different vocabulary, and give users the opportunity to generate a new dbpedia dataset using a custom combination of vocabularies

De Meester, Ben

Dimou, Anastasia

Hellman, S.

Kontokostas, D

Lehmann, J.

Mannens, Erik

Maroy, Wouter

Verborgh, Ruben

English

Meester, Ben de

Kontokostas, Dimitris

Lehmann, Jens

Hellmann, Sebastian

Fraunhofer-ePrints

A vocabulary-independent generation framework for DBpedia and beyond

Ghent University Academic Bibliography

A Vocabulary-Independent GenerationFramework for DBpedia and beyondBen De Meester1, Anastasia Dimou1, Wouter Maroy1, Dimitris Kontokostas2,Ruben Verborgh1, Jens Lehmann3,4, Erik Mannens1, and Sebastian Hellmann21 Ghent University – imec – IDLab,Department of Electronics and Information Systems, Ghent, Belgium{firstname.lastname}@ugent.be2 Leipzig University – AKSW/KILT, Leipzig, Germany{lastname}@informatik.uni-leipzig.de3 University of Bonn, Smart Data Analytics Group, Germanyjens.lehmann@cs.uni-bonn.de4 Fraunhofer IAIS, Sankt Augustin, Germanyjens.lehmann@iais.fraunhofer.deAbstract. The dbpedia Extraction Framework, the generation frame-work behind one of the Linked Open Data cloud’s central hubs, haslimitations which lead to quality issues with the dbpedia dataset. There-fore, we provide a new take on its Extraction Framework that allows fora sustainable and general-purpose Linked Data generation framework byadapting a semantic-driven approach. The proposed approach decouples,in a declarative manner, the extraction, transformation, and mappingrules execution. This way, among others, interchanging different schemaannotations is supported, instead of being coupled to a certain ontologyas it is now, because the dbpedia Extraction Framework allows onlygenerating a certain dataset with a single semantic representation. Inthis paper, we shed more light to the added value that this aspect brings.We provide an extracted dbpedia dataset using a different vocabulary,and give users the opportunity to generate a new dbpedia dataset usinga custom combination of vocabularies.Keywords: DBpedia, FnO, Generation, Linked Data, RML1 IntroductionThe DBpedia Extraction Framework (dbpedia ef) extracts raw data fromWikipedi-a and makes it available as Linked Data, forming the well-known and broadlyused dbpedia dataset [6]. The majority of the dbpedia dataset is derived throughWikipedia infobox templates, after being annotated by the DBpedia ontology5 [6].The rules describing the dbpedia dataset generation from Wikipedia are executedby the dbpedia ef, defined by a worldwide crowd-sourcing effort, and maintainedvia the DBpedia mappings wiki6. Even though dbpedia is one of the central5 http://dbpedia.org/ontology/6 http://mappings.dbpedia.org/index.php/Main_Page2 Ben De Meester et al.interlinking hubs in the Linked Open Data cloud [9], its generation frameworkhas limitations reflected on the generated dataset [8, 10].A major issue is that other schema(s) than the dbpedia ontology cannotbe used to annotate Wikipedia pages. The dbpedia ef functions only with thedbpedia ontology, e.g., the predicate depends on the ontology term used for acertain attribute of an infobox. This occurs because the dbpedia ef selects thecorresponding parser based on where the mapping template is used and whichontology term is selected, e.g., the dbo:date triggers the Date parser.Thus, if an ontology term is not added to the dbpedia ontology, it cannotbe used. For instance, no other predicate than the dbo:location may be used toindicate an entity’s location because no triples will be generated.Other vocabularies, such as the schema.org7 vocabulary, cannot be used unlessthey are imported into the dbpedia ontology, or the dbpedia ef is adjusted8,because, otherwise, it will not recognize the vocabulary’s properties.Similarly, depending on the mapping template and ontology term (predicate)which are used, a different data type can be assigned. For instance, dependingon which predicate is used, the area in square kilometers generates an xsd:doublebut also a dbpedia datatype (dbo:areaTotal) that depends on the used predicate.In this work, we use the semantic general-purpose and more sustainableframework that replaces the current dbpedia ef, which decouples extraction,transformations and mapping execution from the dbpedia ef, and enables gener-ating high quality Linked Data that is not limited to the dbpedia use case, aspresented in detail by Maroy et al. [7]. We specifically demo how this work enablesus to easily make both small and large schema-level changes to the generateddbpedia data without influencing the remainder of the generation process. Thedemo is available at https://rmlio.github.io/dbpedia-ef-schema-demo/.2 Sustainable Linked Data GenerationThe current dbpedia ef is coupled and custom, which hampers maintenance andlimits flexibility with respect to mapping, transformation, and used schema [7].The mapping rules are a custom solution coupled to the dbpedia ef and ontology.Similarly, the data transformations are hard-coded, executed at different placeswithin the dbpedia ef, and coupled with the dbpedia ontology.To alleviate its limitations, the following requirements are proposed [7]:(i) Declarative mapping rules covering all generated rdf triples, and theunderlying implementation can interpret them in each case, whether they referto schema or data transformations [2]. (ii) Decoupled extraction, transfor-mation, and mapping allowing different extraction strategies, transformationlibraries, or mapping rules without requiring adjustments to the underlyingimplementation [2]. (iii) A vocabulary independent solution to annotate theextracted data values, independently of the preferred vocabulary. (iv) Machine-7 http://schema.org8 As done for dcterms (http://purl.org/dc/terms/) and foaf (http://xmlns.com/foaf/0.1/)A Vocabulary-Independent Generation Framework for DBpedia and beyond 3processable mapping rules allow assessment not only for syntax, but also forschema validation [3] or automated mapping rules generation [5].To address these requirements, we developed a solution – explained in detailin [7] – that fulfills the aforementioned requirements built on the rdf MappingLanguage (rml) [4]. rml performs the schema transformations, and is alignedwith fno [1], that performs data transformations. Both schema and data trans-formations are thus covered using declarative machine-processable rules, insteadof coupled, while the wikitext extractor is a separate module, allowing for adecoupled architecture. Most importantly though, the rules are independent ofthe vocabulary used.3 Demo: Interchanging SchemasThe mapping rules which are described in rml, are rdf triples themselves. Thus,they can be updated – automatically or not – and other semantic annotations canbe applied or other datasets can be generated from Wikipedia. Taking advantageof this and relying on the dbpedia mapping rules and the alignment of dbpediaontology with schema.org9, we translated the rml mapping rules for dbpedia touse schema.org and generate another rdf subgraph. More specifically, within theoriginal mapping document, predicates and classes from the dbpedia ontologywere replaced by predicates and classes from schema.org. No further changeswere required, neither for the mapping rules, nor for the data transformations.We executed a new extraction which was done over all 16,244,162 pages inthe English dbpedia that contained articles, templates, media/file descriptions,and primary meta-pages. 191,288 Infobox_persons were found and 1,026,143rdf triples were generated. Indicatively, 179,037 rdf triples were generated withschema:name property, 54,664 with schema:jobTitle, 23,751 with schema:nationality,144,907 with schema:birthPlace, and 139,488 with schema:birthDate. The rdfdataset is available at http://mappings.dbpedia.org/person_schema.dataset.ttl.bz2, and can be interactively queried and compared with the original dbpediadataset on https://rmlio.github.io/dbpedia-ef-schema-demo/.Furthermore, at https://rmlio.github.io/dbpedia-ef-schema-demo/, we pro-vide an interactive Web application that allows users to easily apply small andlarge changes in the dbpedia mapping document to change the schema of theresulting data. Presets are available to easily switch between annotations usingthe dbpedia ontology or schema.org, but users are encouraged to make their ownchanges and create hybrid solutions, or even use completely different ontologiesand vocabularies. Users can trigger the generation of rdf data based on theirapplied changes, to review their adjustments. The application demonstrates thatchanges in the schema remain localized, e.g., changing the predicate does notinfluence which data type is used or which function is executed, i.e., schematransformations are decoupled from vocabulary and data transformations.In this demo paper, we showcase a generic and semantics-driven approach toimprove the Linked Data generation of the current dbpedia ef, as described in9 http://schema.org4 Ben De Meester et al.detail in [7], and provide a proof-of-concept to show the extended possibilities withrespect to schema transformations and how their changes remain decoupled fromthe remainder of the generation framework. Most importantly, the generationoccurs independently of the vocabulary used to semantically annotate the rdfdataset. This is evident by providing a new dbpedia dataset that has all personresources mapped into rdf, however, using schema.org instead of the dbpediaontology. This dataset can be interactively compared to the original dbpediadataset. Furthermore, an interactive application allows users to make small andlarge changes in the schema when generating dbpedia data. They can easilyswitch between different vocabularies used to generate dbpedia data, and createcustom schema transformations.References1. B. De Meester, A. Dimou, R. Verborgh, E. Mannens, and R. Van de Walle. AnOntology to Semantically Declare and Describe Functions. In The Semantic Web:ESWC 2016 Satellite Events, volume 9989 of LNCS, pages 46–49. Springer, 2016.2. B. De Meester, W. Maroy, A. Dimou, R. Verborgh, and E. Mannens. Declarativedata transformations for Linked Data generation: the case of DBpedia. In TheSemantic Web – Latest Advances and New Domains (ESWC 2017). Springer, 2017.3. A. Dimou, D. Kontokostas, M. Freudenberg, R. Verborgh, J. Lehmann, E. Mannens,S. Hellmann, and R. Van de Walle. Assessing and Refining Mappings to rdf toImprove Dataset Quality. In The Semantic Web – ISWC 2015, volume 9367 ofLNCS, pages 133–149. Springer, 2015.4. A. Dimou, M. Vander Sande, P. Colpaert, R. Verborgh, E. Mannens, and R. Van deWalle. RML: A Generic Language for Integrated rdf Mappings of HeterogeneousData. In Proceedings of the 7th Workshop on Linked Data on the Web, volume 1184of CEUR Workshop Proceedings, 2014.5. P. Heyvaert, A. Dimou, A.-L. Herregodts, R. Verborgh, D. Schuurman, E. Mannens,and R. Van de Walle. RMLEditor: A Graph-based Mapping Editor for Linked DataMappings. In The Semantic Web – Latest Advances and New Domains (ESWC2016), volume 9678 of LNCS, pages 709–723. Springer, 2016.6. J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes,S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer. DBpedia – Alarge-scale, multilingual knowledge base extracted from Wikipedia. Sem Web, 2015.7. W. Maroy, A. Dimou, D. Kontokostas, B. De Meester, R. Verborgh, J. Lehmann,E. Mannens, and S. Hellmann. Sustainable linked data generation: The case ofDBpedia. In Proceedings of the 16th International Semantic Web Conference: In-UseTrack, Vienna, Austria, Oct. 2017. Springer.8. H. Paulheim and A. Gangemi. Serving DBpedia with DOLCE – More than JustAdding a Cherry on Top, pages 180–196. Springer International Publishing, 2015.9. M. Schmachtenberg, C. Bizer, and H. Paulheim. Adoption of the Linked Data BestPractices in Different Topical Domains, pages 245–260. Springer, 2014.10. A. Zaveri, D. Kontokostas, M. A. Sherif, L. Bühmann, M. Morsey, S. Auer, andJ. Lehmann. User-driven Quality Evaluation of DBpedia. In Proceedings of the 9thInternational Conference on Semantic Systems, pages 97–104. ACM, 2013.

https://biblio.ugent.be/publication/8540944/file/8540952.pdf

A vocabulary-independent generation framework for DBpedia and beyond

Abstract

Similar works

Full text

Available Versions

Fraunhofer-ePrints

Ghent University Academic Bibliography